Overview

Dataset statistics

Number of variables24
Number of observations19138
Missing cells46006
Missing cells (%)10.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 MiB
Average record size in memory192.0 B

Variable types

Numeric6
Text9
Unsupported2
Categorical7

Alerts

aff_code has constant value ""Constant
page_channel has constant value ""Constant
fuel_type is highly imbalanced (75.2%)Imbalance
msrp has 4686 (24.5%) missing valuesMissing
local_zone has 19138 (100.0%) missing valuesMissing
interior_color has 907 (4.7%) missing valuesMissing
price has 248 (1.3%) missing valuesMissing
price_badge has 19138 (100.0%) missing valuesMissing
trim has 315 (1.6%) missing valuesMissing
mileage has 554 (2.9%) missing valuesMissing
cat has 210 (1.1%) missing valuesMissing
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
local_zone is an unsupported type, check if it needs cleaning or further analysisUnsupported
price_badge is an unsupported type, check if it needs cleaning or further analysisUnsupported
msrp has 3586 (18.7%) zerosZeros
mileage has 708 (3.7%) zerosZeros

Reproduction

Analysis started2024-04-25 00:20:47.837227
Analysis finished2024-04-25 00:20:55.109472
Duration7.27 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct19138
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9568.5
Minimum0
Maximum19137
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size149.6 KiB
2024-04-24T19:20:55.385204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile956.85
Q14784.25
median9568.5
Q314352.75
95-th percentile18180.15
Maximum19137
Range19137
Interquartile range (IQR)9568.5

Descriptive statistics

Standard deviation5524.8091
Coefficient of variation (CV)0.57739552
Kurtosis-1.2
Mean9568.5
Median Absolute Deviation (MAD)4784.5
Skewness0
Sum1.8312195 × 108
Variance30523515
MonotonicityStrictly increasing
2024-04-24T19:20:55.577386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
12764 1
 
< 0.1%
12762 1
 
< 0.1%
12761 1
 
< 0.1%
12760 1
 
< 0.1%
12759 1
 
< 0.1%
12758 1
 
< 0.1%
12757 1
 
< 0.1%
12756 1
 
< 0.1%
12755 1
 
< 0.1%
Other values (19128) 19128
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
19137 1
< 0.1%
19136 1
< 0.1%
19135 1
< 0.1%
19134 1
< 0.1%
19133 1
< 0.1%
19132 1
< 0.1%
19131 1
< 0.1%
19130 1
< 0.1%
19129 1
< 0.1%
19128 1
< 0.1%

msrp
Real number (ℝ)

MISSING  ZEROS 

Distinct3520
Distinct (%)24.4%
Missing4686
Missing (%)24.5%
Infinite0
Infinite (%)0.0%
Mean38825.187
Minimum0
Maximum329486
Zeros3586
Zeros (%)18.7%
Negative0
Negative (%)0.0%
Memory size149.6 KiB
2024-04-24T19:20:55.760254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114940
median36748
Q354595
95-th percentile94105.15
Maximum329486
Range329486
Interquartile range (IQR)39655

Descriptive statistics

Standard deviation32152.783
Coefficient of variation (CV)0.82814239
Kurtosis6.2152576
Mean38825.187
Median Absolute Deviation (MAD)17847
Skewness1.4056051
Sum5.6110161 × 108
Variance1.0338015 × 109
MonotonicityNot monotonic
2024-04-24T19:20:55.933654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3586
 
18.7%
36926 100
 
0.5%
46465 84
 
0.4%
44115 81
 
0.4%
54595 67
 
0.4%
26980 61
 
0.3%
33160 58
 
0.3%
38726 52
 
0.3%
25080 46
 
0.2%
35980 46
 
0.2%
Other values (3510) 10271
53.7%
(Missing) 4686
24.5%
ValueCountFrequency (%)
0 3586
18.7%
6000 2
 
< 0.1%
7705 2
 
< 0.1%
7985 2
 
< 0.1%
9000 2
 
< 0.1%
9139 2
 
< 0.1%
9985 2
 
< 0.1%
10985 2
 
< 0.1%
10995 2
 
< 0.1%
11991 2
 
< 0.1%
ValueCountFrequency (%)
329486 2
< 0.1%
317486 2
< 0.1%
311895 2
< 0.1%
298875 2
< 0.1%
288300 2
< 0.1%
287400 1
< 0.1%
270710 1
< 0.1%
251160 2
< 0.1%
249320 1
< 0.1%
239600 2
< 0.1%

year
Real number (ℝ)

Distinct57
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.7547
Minimum1936
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size149.6 KiB
2024-04-24T19:20:56.144201image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1936
5-th percentile2009
Q12020
median2024
Q32024
95-th percentile2024
Maximum2025
Range89
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.0393611
Coefficient of variation (CV)0.0029886661
Kurtosis24.261979
Mean2020.7547
Median Absolute Deviation (MAD)0
Skewness-3.7811783
Sum38673204
Variance36.473883
MonotonicityNot monotonic
2024-04-24T19:20:56.350864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2024 9927
51.9%
2023 1586
 
8.3%
2021 1327
 
6.9%
2020 795
 
4.2%
2022 709
 
3.7%
2018 605
 
3.2%
2019 568
 
3.0%
2017 510
 
2.7%
2016 446
 
2.3%
2015 369
 
1.9%
Other values (47) 2296
 
12.0%
ValueCountFrequency (%)
1936 2
 
< 0.1%
1957 8
< 0.1%
1959 2
 
< 0.1%
1960 2
 
< 0.1%
1964 2
 
< 0.1%
1965 7
< 0.1%
1967 2
 
< 0.1%
1969 2
 
< 0.1%
1970 2
 
< 0.1%
1972 8
< 0.1%
ValueCountFrequency (%)
2025 198
 
1.0%
2024 9927
51.9%
2023 1586
 
8.3%
2022 709
 
3.7%
2021 1327
 
6.9%
2020 795
 
4.2%
2019 568
 
3.0%
2018 605
 
3.2%
2017 510
 
2.7%
2016 446
 
2.3%
Distinct4070
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Memory size149.6 KiB
2024-04-24T19:20:56.688027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length102
Median length88
Mean length28.547393
Min length12

Characters and Unicode

Total characters546340
Distinct characters79
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique280 ?
Unique (%)1.5%

Sample

1st rowChevrolet:Blazer EV:RS:2024
2nd rowRAM:ProMaster 2500:High Roof:2024
3rd rowMercedes-Benz:Sprinter 2500:High Roof:2024
4th rowHonda:CR-V:EX:2024
5th rowChevrolet:Equinox:LS:2024
ValueCountFrequency (%)
volkswagen:tiguan:2.0t 921
 
2.5%
se:2024 609
 
1.6%
ford:bronco 457
 
1.2%
r-line 338
 
0.9%
chevrolet:silverado 335
 
0.9%
gmc:sierra 328
 
0.9%
black:2024 310
 
0.8%
mercedes-benz:amg 304
 
0.8%
se 296
 
0.8%
jeep:grand 282
 
0.8%
Other values (4559) 33321
88.9%
2024-04-24T19:20:57.210860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 57414
 
10.5%
2 37963
 
6.9%
e 34880
 
6.4%
0 29323
 
5.4%
a 26129
 
4.8%
r 25688
 
4.7%
o 20544
 
3.8%
i 19043
 
3.5%
18343
 
3.4%
n 16268
 
3.0%
Other values (69) 260745
47.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 546340
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
: 57414
 
10.5%
2 37963
 
6.9%
e 34880
 
6.4%
0 29323
 
5.4%
a 26129
 
4.8%
r 25688
 
4.7%
o 20544
 
3.8%
i 19043
 
3.5%
18343
 
3.4%
n 16268
 
3.0%
Other values (69) 260745
47.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 546340
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
: 57414
 
10.5%
2 37963
 
6.9%
e 34880
 
6.4%
0 29323
 
5.4%
a 26129
 
4.8%
r 25688
 
4.7%
o 20544
 
3.8%
i 19043
 
3.5%
18343
 
3.4%
n 16268
 
3.0%
Other values (69) 260745
47.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 546340
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
: 57414
 
10.5%
2 37963
 
6.9%
e 34880
 
6.4%
0 29323
 
5.4%
a 26129
 
4.8%
r 25688
 
4.7%
o 20544
 
3.8%
i 19043
 
3.5%
18343
 
3.4%
n 16268
 
3.0%
Other values (69) 260745
47.7%

model
Text

Distinct855
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size149.6 KiB
2024-04-24T19:20:57.540158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length28
Median length24
Mean length7.3020692
Min length1

Characters and Unicode

Total characters139747
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)0.1%

Sample

1st rowBlazer EV
2nd rowProMaster 2500
3rd rowSprinter 2500
4th rowCR-V
5th rowEquinox
ValueCountFrequency (%)
tiguan 923
 
3.6%
escape 679
 
2.6%
2500 667
 
2.6%
sport 608
 
2.4%
1500 580
 
2.2%
trax 523
 
2.0%
bronco 480
 
1.9%
hornet 410
 
1.6%
xt5 389
 
1.5%
equinox 373
 
1.4%
Other values (738) 20188
78.2%
2024-04-24T19:20:58.101469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 11236
 
8.0%
r 10231
 
7.3%
e 8894
 
6.4%
o 6888
 
4.9%
6682
 
4.8%
n 6413
 
4.6%
0 5531
 
4.0%
i 5319
 
3.8%
t 5265
 
3.8%
s 4512
 
3.2%
Other values (60) 68776
49.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 139747
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 11236
 
8.0%
r 10231
 
7.3%
e 8894
 
6.4%
o 6888
 
4.9%
6682
 
4.8%
n 6413
 
4.6%
0 5531
 
4.0%
i 5319
 
3.8%
t 5265
 
3.8%
s 4512
 
3.2%
Other values (60) 68776
49.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 139747
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 11236
 
8.0%
r 10231
 
7.3%
e 8894
 
6.4%
o 6888
 
4.9%
6682
 
4.8%
n 6413
 
4.6%
0 5531
 
4.0%
i 5319
 
3.8%
t 5265
 
3.8%
s 4512
 
3.2%
Other values (60) 68776
49.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 139747
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 11236
 
8.0%
r 10231
 
7.3%
e 8894
 
6.4%
o 6888
 
4.9%
6682
 
4.8%
n 6413
 
4.6%
0 5531
 
4.0%
i 5319
 
3.8%
t 5265
 
3.8%
s 4512
 
3.2%
Other values (60) 68776
49.2%

local_zone
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing19138
Missing (%)100.0%
Memory size149.6 KiB

interior_color
Text

MISSING 

Distinct710
Distinct (%)3.9%
Missing907
Missing (%)4.7%
Memory size149.6 KiB
2024-04-24T19:20:58.401262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length47
Median length43
Mean length7.9224947
Min length2

Characters and Unicode

Total characters144435
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)0.2%

Sample

1st rowBlack
2nd rowBlack
3rd rowGray
4th rowMedium Ash Gray
5th rowPearl Beige
ValueCountFrequency (%)
black 10216
38.2%
jet 1785
 
6.7%
gray 1760
 
6.6%
ebony 1531
 
5.7%
charcoal 739
 
2.8%
beige 483
 
1.8%
medium 474
 
1.8%
red 454
 
1.7%
444
 
1.7%
onyx 411
 
1.5%
Other values (525) 8460
31.6%
2024-04-24T19:20:58.890020image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 18289
12.7%
l 12824
 
8.9%
c 12696
 
8.8%
B 11820
 
8.2%
k 11107
 
7.7%
8540
 
5.9%
e 8195
 
5.7%
r 5926
 
4.1%
t 5283
 
3.7%
n 5159
 
3.6%
Other values (57) 44596
30.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 144435
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 18289
12.7%
l 12824
 
8.9%
c 12696
 
8.8%
B 11820
 
8.2%
k 11107
 
7.7%
8540
 
5.9%
e 8195
 
5.7%
r 5926
 
4.1%
t 5283
 
3.7%
n 5159
 
3.6%
Other values (57) 44596
30.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 144435
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 18289
12.7%
l 12824
 
8.9%
c 12696
 
8.8%
B 11820
 
8.2%
k 11107
 
7.7%
8540
 
5.9%
e 8195
 
5.7%
r 5926
 
4.1%
t 5283
 
3.7%
n 5159
 
3.6%
Other values (57) 44596
30.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 144435
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 18289
12.7%
l 12824
 
8.9%
c 12696
 
8.8%
B 11820
 
8.2%
k 11107
 
7.7%
8540
 
5.9%
e 8195
 
5.7%
r 5926
 
4.1%
t 5283
 
3.7%
n 5159
 
3.6%
Other values (57) 44596
30.9%

aff_code
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size149.6 KiB
national
19138 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters153104
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownational
2nd rownational
3rd rownational
4th rownational
5th rownational

Common Values

ValueCountFrequency (%)
national 19138
100.0%

Length

2024-04-24T19:20:59.073472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-24T19:20:59.227684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
national 19138
100.0%

Most occurring characters

ValueCountFrequency (%)
n 38276
25.0%
a 38276
25.0%
t 19138
12.5%
i 19138
12.5%
o 19138
12.5%
l 19138
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 153104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 38276
25.0%
a 38276
25.0%
t 19138
12.5%
i 19138
12.5%
o 19138
12.5%
l 19138
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 153104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 38276
25.0%
a 38276
25.0%
t 19138
12.5%
i 19138
12.5%
o 19138
12.5%
l 19138
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 153104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 38276
25.0%
a 38276
25.0%
t 19138
12.5%
i 19138
12.5%
o 19138
12.5%
l 19138
12.5%

price
Real number (ℝ)

MISSING 

Distinct6415
Distinct (%)34.0%
Missing248
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean43913.221
Minimum0
Maximum1699800
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size149.6 KiB
2024-04-24T19:20:59.429427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10980
Q125335
median35003
Q351873
95-th percentile97145
Maximum1699800
Range1699800
Interquartile range (IQR)26538

Descriptive statistics

Standard deviation41443.429
Coefficient of variation (CV)0.94375744
Kurtosis311.22747
Mean43913.221
Median Absolute Deviation (MAD)12513
Skewness11.133983
Sum8.2952075 × 108
Variance1.7175578 × 109
MonotonicityNot monotonic
2024-04-24T19:20:59.744328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49115 85
 
0.4%
51465 82
 
0.4%
33504 62
 
0.3%
9995 53
 
0.3%
26980 52
 
0.3%
16995 50
 
0.3%
23885 46
 
0.2%
15995 46
 
0.2%
18995 45
 
0.2%
19995 44
 
0.2%
Other values (6405) 18325
95.8%
(Missing) 248
 
1.3%
ValueCountFrequency (%)
0 4
< 0.1%
32 1
 
< 0.1%
1500 2
< 0.1%
1700 2
< 0.1%
2000 2
< 0.1%
2900 2
< 0.1%
2990 2
< 0.1%
3000 2
< 0.1%
3350 2
< 0.1%
3440 2
< 0.1%
ValueCountFrequency (%)
1699800 2
< 0.1%
829800 1
< 0.1%
709800 2
< 0.1%
639800 2
< 0.1%
579999 1
< 0.1%
569895 2
< 0.1%
569800 2
< 0.1%
519900 2
< 0.1%
489800 2
< 0.1%
459999 2
< 0.1%

price_badge
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing19138
Missing (%)100.0%
Memory size149.6 KiB

trim
Text

MISSING 

Distinct1159
Distinct (%)6.2%
Missing315
Missing (%)1.6%
Memory size149.6 KiB
2024-04-24T19:21:00.156932image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length75
Median length65
Mean length8.0118472
Min length1

Characters and Unicode

Total characters150807
Distinct characters78
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)0.3%

Sample

1st rowRS
2nd rowHigh Roof
3rd rowHigh Roof
4th rowEX
5th rowLS
ValueCountFrequency (%)
base 1563
 
5.2%
premium 1330
 
4.4%
se 1214
 
4.0%
2.0t 1164
 
3.8%
s 777
 
2.6%
limited 728
 
2.4%
4matic 616
 
2.0%
sport 547
 
1.8%
luxury 545
 
1.8%
sel 540
 
1.8%
Other values (831) 21278
70.2%
2024-04-24T19:21:00.807952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 12739
 
8.4%
11459
 
7.6%
i 9303
 
6.2%
r 7663
 
5.1%
T 6394
 
4.2%
S 6230
 
4.1%
a 6137
 
4.1%
L 5872
 
3.9%
u 4771
 
3.2%
m 4438
 
2.9%
Other values (68) 75801
50.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 150807
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 12739
 
8.4%
11459
 
7.6%
i 9303
 
6.2%
r 7663
 
5.1%
T 6394
 
4.2%
S 6230
 
4.1%
a 6137
 
4.1%
L 5872
 
3.9%
u 4771
 
3.2%
m 4438
 
2.9%
Other values (68) 75801
50.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 150807
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 12739
 
8.4%
11459
 
7.6%
i 9303
 
6.2%
r 7663
 
5.1%
T 6394
 
4.2%
S 6230
 
4.1%
a 6137
 
4.1%
L 5872
 
3.9%
u 4771
 
3.2%
m 4438
 
2.9%
Other values (68) 75801
50.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 150807
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 12739
 
8.4%
11459
 
7.6%
i 9303
 
6.2%
r 7663
 
5.1%
T 6394
 
4.2%
S 6230
 
4.1%
a 6137
 
4.1%
L 5872
 
3.9%
u 4771
 
3.2%
m 4438
 
2.9%
Other values (68) 75801
50.3%

drivetrain
Categorical

Distinct9
Distinct (%)< 0.1%
Missing103
Missing (%)0.5%
Memory size149.6 KiB
All-wheel Drive
9212 
Front-wheel Drive
3726 
Four-wheel Drive
3245 
Rear-wheel Drive
1943 
AWD
 
548
Other values (4)
 
361

Length

Max length17
Median length16
Mean length15.098765
Min length3

Characters and Unicode

Total characters287405
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAll-wheel Drive
2nd rowFront-wheel Drive
3rd rowRear-wheel Drive
4th rowFront-wheel Drive
5th rowFront-wheel Drive

Common Values

ValueCountFrequency (%)
All-wheel Drive 9212
48.1%
Front-wheel Drive 3726
19.5%
Four-wheel Drive 3245
 
17.0%
Rear-wheel Drive 1943
 
10.2%
AWD 548
 
2.9%
FWD 190
 
1.0%
4WD 103
 
0.5%
Unknown 37
 
0.2%
RWD 31
 
0.2%
(Missing) 103
 
0.5%

Length

2024-04-24T19:21:01.003550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-24T19:21:01.160525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
drive 18126
48.8%
all-wheel 9212
24.8%
front-wheel 3726
 
10.0%
four-wheel 3245
 
8.7%
rear-wheel 1943
 
5.2%
awd 548
 
1.5%
fwd 190
 
0.5%
4wd 103
 
0.3%
unknown 37
 
0.1%
rwd 31
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 56321
19.6%
l 36550
12.7%
r 27040
9.4%
D 18998
 
6.6%
w 18163
 
6.3%
- 18126
 
6.3%
h 18126
 
6.3%
18126
 
6.3%
i 18126
 
6.3%
v 18126
 
6.3%
Other values (12) 39703
13.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 287405
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 56321
19.6%
l 36550
12.7%
r 27040
9.4%
D 18998
 
6.6%
w 18163
 
6.3%
- 18126
 
6.3%
h 18126
 
6.3%
18126
 
6.3%
i 18126
 
6.3%
v 18126
 
6.3%
Other values (12) 39703
13.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 287405
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 56321
19.6%
l 36550
12.7%
r 27040
9.4%
D 18998
 
6.6%
w 18163
 
6.3%
- 18126
 
6.3%
h 18126
 
6.3%
18126
 
6.3%
i 18126
 
6.3%
v 18126
 
6.3%
Other values (12) 39703
13.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 287405
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 56321
19.6%
l 36550
12.7%
r 27040
9.4%
D 18998
 
6.6%
w 18163
 
6.3%
- 18126
 
6.3%
h 18126
 
6.3%
18126
 
6.3%
i 18126
 
6.3%
v 18126
 
6.3%
Other values (12) 39703
13.8%
Distinct427
Distinct (%)2.2%
Missing154
Missing (%)0.8%
Memory size149.6 KiB
2024-04-24T19:21:01.531278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length86
Median length49
Mean length24.086705
Min length6

Characters and Unicode

Total characters457262
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)0.1%

Sample

1st rowCastle Rock Chevrolet GMC
2nd rowNew Smyrna Chrysler Jeep Dodge RAM
3rd rowMercedes-Benz of Farmington
4th rowKingman Honda
5th rowMcSweeney Chevrolet GMC Clanton
ValueCountFrequency (%)
of 6083
 
8.7%
ford 2351
 
3.3%
chevrolet 1748
 
2.5%
chicago 1698
 
2.4%
auto 1468
 
2.1%
dodge 1323
 
1.9%
ram 1292
 
1.8%
jeep 1292
 
1.8%
chrysler 1292
 
1.8%
volkswagen 1183
 
1.7%
Other values (416) 50490
71.9%
2024-04-24T19:21:02.069712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
51236
 
11.2%
o 37705
 
8.2%
e 35522
 
7.8%
a 30049
 
6.6%
r 27812
 
6.1%
l 22164
 
4.8%
n 20319
 
4.4%
i 19173
 
4.2%
t 18921
 
4.1%
s 17122
 
3.7%
Other values (57) 177239
38.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 457262
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
51236
 
11.2%
o 37705
 
8.2%
e 35522
 
7.8%
a 30049
 
6.6%
r 27812
 
6.1%
l 22164
 
4.8%
n 20319
 
4.4%
i 19173
 
4.2%
t 18921
 
4.1%
s 17122
 
3.7%
Other values (57) 177239
38.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 457262
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
51236
 
11.2%
o 37705
 
8.2%
e 35522
 
7.8%
a 30049
 
6.6%
r 27812
 
6.1%
l 22164
 
4.8%
n 20319
 
4.4%
i 19173
 
4.2%
t 18921
 
4.1%
s 17122
 
3.7%
Other values (57) 177239
38.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 457262
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
51236
 
11.2%
o 37705
 
8.2%
e 35522
 
7.8%
a 30049
 
6.6%
r 27812
 
6.1%
l 22164
 
4.8%
n 20319
 
4.4%
i 19173
 
4.2%
t 18921
 
4.1%
s 17122
 
3.7%
Other values (57) 177239
38.8%

dealer_zip
Real number (ℝ)

Distinct141
Distinct (%)0.7%
Missing154
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean59792.083
Minimum11361
Maximum99301
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size149.6 KiB
2024-04-24T19:21:02.252514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum11361
5-th percentile60004
Q160126
median60445
Q360540
95-th percentile60647
Maximum99301
Range87940
Interquartile range (IQR)414

Descriptive statistics

Standard deviation3049.2189
Coefficient of variation (CV)0.050997034
Kurtosis34.310769
Mean59792.083
Median Absolute Deviation (MAD)196
Skewness-3.9667216
Sum1.1350929 × 109
Variance9297735.9
MonotonicityNot monotonic
2024-04-24T19:21:02.453997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60540 1343
 
7.0%
60515 959
 
5.0%
60525 637
 
3.3%
60453 593
 
3.1%
46322 578
 
3.0%
60126 570
 
3.0%
60025 469
 
2.5%
60559 455
 
2.4%
60035 448
 
2.3%
60610 416
 
2.2%
Other values (131) 12516
65.4%
ValueCountFrequency (%)
11361 1
 
< 0.1%
17042 1
 
< 0.1%
20904 3
< 0.1%
26101 1
 
< 0.1%
30028 3
< 0.1%
30157 1
 
< 0.1%
31210 2
< 0.1%
32168 1
 
< 0.1%
34997 3
< 0.1%
35045 1
 
< 0.1%
ValueCountFrequency (%)
99301 1
 
< 0.1%
95661 1
 
< 0.1%
94596 3
< 0.1%
89146 1
 
< 0.1%
86409 2
< 0.1%
86004 1
 
< 0.1%
84025 1
 
< 0.1%
82001 2
< 0.1%
80221 1
 
< 0.1%
80104 1
 
< 0.1%

mileage
Real number (ℝ)

MISSING  ZEROS 

Distinct4624
Distinct (%)24.9%
Missing554
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean26047.929
Minimum0
Maximum440911
Zeros708
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size149.6 KiB
2024-04-24T19:21:02.615968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median27
Q341725
95-th percentile113174.25
Maximum440911
Range440911
Interquartile range (IQR)41720

Descriptive statistics

Standard deviation41874.321
Coefficient of variation (CV)1.6075874
Kurtosis6.1633143
Mean26047.929
Median Absolute Deviation (MAD)27
Skewness2.1076472
Sum4.8407471 × 108
Variance1.7534588 × 109
MonotonicityNot monotonic
2024-04-24T19:21:02.882504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 1317
 
6.9%
5 1292
 
6.8%
3 1020
 
5.3%
0 708
 
3.7%
6 668
 
3.5%
2 662
 
3.5%
1 559
 
2.9%
7 536
 
2.8%
4 469
 
2.5%
11 360
 
1.9%
Other values (4614) 10993
57.4%
(Missing) 554
 
2.9%
ValueCountFrequency (%)
0 708
3.7%
1 559
2.9%
2 662
3.5%
3 1020
5.3%
4 469
 
2.5%
5 1292
6.8%
6 668
3.5%
7 536
2.8%
8 325
 
1.7%
9 243
 
1.3%
ValueCountFrequency (%)
440911 2
< 0.1%
426586 2
< 0.1%
385223 2
< 0.1%
350017 2
< 0.1%
317568 2
< 0.1%
304425 2
< 0.1%
274023 2
< 0.1%
268470 1
< 0.1%
265649 2
< 0.1%
241421 2
< 0.1%

make
Text

Distinct60
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size149.6 KiB
2024-04-24T19:21:03.183596image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length13
Median length10
Mean length6.3653464
Min length3

Characters and Unicode

Total characters121820
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChevrolet
2nd rowRAM
3rd rowMercedes-Benz
4th rowHonda
5th rowChevrolet
ValueCountFrequency (%)
ford 2587
 
13.4%
chevrolet 2059
 
10.6%
volkswagen 1541
 
8.0%
bmw 1298
 
6.7%
mercedes-benz 1065
 
5.5%
subaru 808
 
4.2%
cadillac 805
 
4.2%
nissan 803
 
4.2%
jeep 674
 
3.5%
dodge 658
 
3.4%
Other values (54) 7042
36.4%
2024-04-24T19:21:03.603851image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 13247
 
10.9%
o 10018
 
8.2%
a 8756
 
7.2%
r 7794
 
6.4%
d 7277
 
6.0%
l 6352
 
5.2%
n 5858
 
4.8%
s 5325
 
4.4%
i 4421
 
3.6%
M 3952
 
3.2%
Other values (36) 48820
40.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121820
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 13247
 
10.9%
o 10018
 
8.2%
a 8756
 
7.2%
r 7794
 
6.4%
d 7277
 
6.0%
l 6352
 
5.2%
n 5858
 
4.8%
s 5325
 
4.4%
i 4421
 
3.6%
M 3952
 
3.2%
Other values (36) 48820
40.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121820
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 13247
 
10.9%
o 10018
 
8.2%
a 8756
 
7.2%
r 7794
 
6.4%
d 7277
 
6.0%
l 6352
 
5.2%
n 5858
 
4.8%
s 5325
 
4.4%
i 4421
 
3.6%
M 3952
 
3.2%
Other values (36) 48820
40.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121820
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 13247
 
10.9%
o 10018
 
8.2%
a 8756
 
7.2%
r 7794
 
6.4%
d 7277
 
6.0%
l 6352
 
5.2%
n 5858
 
4.8%
s 5325
 
4.4%
i 4421
 
3.6%
M 3952
 
3.2%
Other values (36) 48820
40.1%

bodystyle
Categorical

Distinct10
Distinct (%)0.1%
Missing100
Missing (%)0.5%
Memory size149.6 KiB
SUV
11293 
Sedan
3121 
Pickup Truck
1625 
Coupe
 
756
Cargo Van
 
727
Other values (5)
1516 

Length

Max length13
Median length3
Mean length4.9319781
Min length3

Characters and Unicode

Total characters93895
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSUV
2nd rowCargo Van
3rd rowCargo Van
4th rowSUV
5th rowSUV

Common Values

ValueCountFrequency (%)
SUV 11293
59.0%
Sedan 3121
 
16.3%
Pickup Truck 1625
 
8.5%
Coupe 756
 
4.0%
Cargo Van 727
 
3.8%
Convertible 603
 
3.2%
Hatchback 531
 
2.8%
Wagon 202
 
1.1%
Passenger Van 151
 
0.8%
Minivan 29
 
0.2%
(Missing) 100
 
0.5%

Length

2024-04-24T19:21:03.809486image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-24T19:21:03.990408image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
suv 11293
52.4%
sedan 3121
 
14.5%
pickup 1625
 
7.5%
truck 1625
 
7.5%
van 878
 
4.1%
coupe 756
 
3.5%
cargo 727
 
3.4%
convertible 603
 
2.8%
hatchback 531
 
2.5%
wagon 202
 
0.9%
Other values (2) 180
 
0.8%

Most occurring characters

ValueCountFrequency (%)
S 14414
15.4%
V 12171
13.0%
U 11293
12.0%
a 6170
 
6.6%
e 5385
 
5.7%
n 5013
 
5.3%
c 4312
 
4.6%
u 4006
 
4.3%
k 3781
 
4.0%
d 3121
 
3.3%
Other values (18) 24229
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 93895
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 14414
15.4%
V 12171
13.0%
U 11293
12.0%
a 6170
 
6.6%
e 5385
 
5.7%
n 5013
 
5.3%
c 4312
 
4.6%
u 4006
 
4.3%
k 3781
 
4.0%
d 3121
 
3.3%
Other values (18) 24229
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 93895
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 14414
15.4%
V 12171
13.0%
U 11293
12.0%
a 6170
 
6.6%
e 5385
 
5.7%
n 5013
 
5.3%
c 4312
 
4.6%
u 4006
 
4.3%
k 3781
 
4.0%
d 3121
 
3.3%
Other values (18) 24229
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 93895
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 14414
15.4%
V 12171
13.0%
U 11293
12.0%
a 6170
 
6.6%
e 5385
 
5.7%
n 5013
 
5.3%
c 4312
 
4.6%
u 4006
 
4.3%
k 3781
 
4.0%
d 3121
 
3.3%
Other values (18) 24229
25.8%

cat
Categorical

MISSING 

Distinct37
Distinct (%)0.2%
Missing210
Missing (%)1.1%
Memory size149.6 KiB
crossover_compact
4192 
luxurysuv_crossover
2226 
crossover_midsize
1334 
truck_fullsize
1226 
suv_midsize
990 
Other values (32)
8960 

Length

Max length28
Median length24
Mean length16.529533
Min length8

Characters and Unicode

Total characters312871
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowev_crossover_midsize
2nd rowvan_fullsize
3rd rowvan_fullsize
4th rowcrossover_compact
5th rowcrossover_midsize

Common Values

ValueCountFrequency (%)
crossover_compact 4192
21.9%
luxurysuv_crossover 2226
11.6%
crossover_midsize 1334
 
7.0%
truck_fullsize 1226
 
6.4%
suv_midsize 990
 
5.2%
luxurypassenger_standard 929
 
4.9%
hybrid_suv 919
 
4.8%
van_fullsize 818
 
4.3%
sedan_compact 815
 
4.3%
luxurypassenger_plus 787
 
4.1%
Other values (27) 4692
24.5%

Length

2024-04-24T19:21:04.229330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
crossover_compact 4192
22.1%
luxurysuv_crossover 2226
11.8%
crossover_midsize 1334
 
7.0%
truck_fullsize 1226
 
6.5%
suv_midsize 990
 
5.2%
luxurypassenger_standard 929
 
4.9%
hybrid_suv 919
 
4.9%
van_fullsize 818
 
4.3%
sedan_compact 815
 
4.3%
luxurypassenger_plus 787
 
4.2%
Other values (27) 4692
24.8%

Most occurring characters

ValueCountFrequency (%)
s 36790
11.8%
r 28762
 
9.2%
o 24409
 
7.8%
e 24137
 
7.7%
c 24134
 
7.7%
u 22425
 
7.2%
_ 20108
 
6.4%
v 17473
 
5.6%
a 14816
 
4.7%
i 11855
 
3.8%
Other values (15) 87962
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 312871
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 36790
11.8%
r 28762
 
9.2%
o 24409
 
7.8%
e 24137
 
7.7%
c 24134
 
7.7%
u 22425
 
7.2%
_ 20108
 
6.4%
v 17473
 
5.6%
a 14816
 
4.7%
i 11855
 
3.8%
Other values (15) 87962
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 312871
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 36790
11.8%
r 28762
 
9.2%
o 24409
 
7.8%
e 24137
 
7.7%
c 24134
 
7.7%
u 22425
 
7.2%
_ 20108
 
6.4%
v 17473
 
5.6%
a 14816
 
4.7%
i 11855
 
3.8%
Other values (15) 87962
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 312871
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 36790
11.8%
r 28762
 
9.2%
o 24409
 
7.8%
e 24137
 
7.7%
c 24134
 
7.7%
u 22425
 
7.2%
_ 20108
 
6.4%
v 17473
 
5.6%
a 14816
 
4.7%
i 11855
 
3.8%
Other values (15) 87962
28.1%

vin
Text

Distinct10061
Distinct (%)52.6%
Missing0
Missing (%)0.0%
Memory size149.6 KiB
2024-04-24T19:21:04.494337image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length17
Median length17
Mean length16.993939
Min length10

Characters and Unicode

Total characters325230
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique984 ?
Unique (%)5.1%

Sample

1st row3GNKDCRJ6RS227894
2nd row3C6LRVDG0RE118763
3rd rowW1Y4KCHY8RT178723
4th row5J6RS3H44RL004214
5th row3GNAXHEG1RL299011
ValueCountFrequency (%)
1fmcu9mn1rua60138 2
 
< 0.1%
1g6dw677950218907 2
 
< 0.1%
1g2mg35x07y138075 2
 
< 0.1%
wz1db0c00nw048997 2
 
< 0.1%
w04gr6sx1k1011517 2
 
< 0.1%
jnkay01fx8m653034 2
 
< 0.1%
1g6dw67v980197947 2
 
< 0.1%
1g6dc67a250157114 2
 
< 0.1%
zffyr51a9x0116838 2
 
< 0.1%
1g1bl52pxtr108710 2
 
< 0.1%
Other values (10051) 19118
99.9%
2024-04-24T19:21:05.023579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 25562
 
7.9%
3 21151
 
6.5%
0 19091
 
5.9%
2 18453
 
5.7%
4 16765
 
5.2%
5 16500
 
5.1%
7 16102
 
5.0%
R 14817
 
4.6%
6 14263
 
4.4%
8 13638
 
4.2%
Other values (26) 148888
45.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 325230
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 25562
 
7.9%
3 21151
 
6.5%
0 19091
 
5.9%
2 18453
 
5.7%
4 16765
 
5.2%
5 16500
 
5.1%
7 16102
 
5.0%
R 14817
 
4.6%
6 14263
 
4.4%
8 13638
 
4.2%
Other values (26) 148888
45.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 325230
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 25562
 
7.9%
3 21151
 
6.5%
0 19091
 
5.9%
2 18453
 
5.7%
4 16765
 
5.2%
5 16500
 
5.1%
7 16102
 
5.0%
R 14817
 
4.6%
6 14263
 
4.4%
8 13638
 
4.2%
Other values (26) 148888
45.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 325230
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 25562
 
7.9%
3 21151
 
6.5%
0 19091
 
5.9%
2 18453
 
5.7%
4 16765
 
5.2%
5 16500
 
5.1%
7 16102
 
5.0%
R 14817
 
4.6%
6 14263
 
4.4%
8 13638
 
4.2%
Other values (26) 148888
45.8%
Distinct2491
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size149.6 KiB
2024-04-24T19:21:05.437696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length97
Median length83
Mean length23.547393
Min length7

Characters and Unicode

Total characters450650
Distinct characters79
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique135 ?
Unique (%)0.7%

Sample

1st rowChevrolet:Blazer EV:RS
2nd rowRAM:ProMaster 2500:High Roof
3rd rowMercedes-Benz:Sprinter 2500:High Roof
4th rowHonda:CR-V:EX
5th rowChevrolet:Equinox:LS
ValueCountFrequency (%)
se 975
 
2.6%
volkswagen:tiguan:2.0t 921
 
2.5%
4matic 502
 
1.3%
plus 501
 
1.3%
s 491
 
1.3%
ford:bronco 457
 
1.2%
premium 447
 
1.2%
r-line 441
 
1.2%
black 378
 
1.0%
chevrolet:silverado 335
 
0.9%
Other values (2542) 32053
85.5%
2024-04-24T19:21:06.178413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 38276
 
8.5%
e 34880
 
7.7%
a 26129
 
5.8%
r 25688
 
5.7%
o 20544
 
4.6%
i 19043
 
4.2%
18343
 
4.1%
n 16268
 
3.6%
s 13528
 
3.0%
l 12588
 
2.8%
Other values (69) 225363
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 450650
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
: 38276
 
8.5%
e 34880
 
7.7%
a 26129
 
5.8%
r 25688
 
5.7%
o 20544
 
4.6%
i 19043
 
4.2%
18343
 
4.1%
n 16268
 
3.6%
s 13528
 
3.0%
l 12588
 
2.8%
Other values (69) 225363
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 450650
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
: 38276
 
8.5%
e 34880
 
7.7%
a 26129
 
5.8%
r 25688
 
5.7%
o 20544
 
4.6%
i 19043
 
4.2%
18343
 
4.1%
n 16268
 
3.6%
s 13528
 
3.0%
l 12588
 
2.8%
Other values (69) 225363
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 450650
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
: 38276
 
8.5%
e 34880
 
7.7%
a 26129
 
5.8%
r 25688
 
5.7%
o 20544
 
4.6%
i 19043
 
4.2%
18343
 
4.1%
n 16268
 
3.6%
s 13528
 
3.0%
l 12588
 
2.8%
Other values (69) 225363
50.0%

fuel_type
Categorical

IMBALANCE 

Distinct13
Distinct (%)0.1%
Missing129
Missing (%)0.7%
Memory size149.6 KiB
Gasoline
16058 
Electric
 
1199
Hybrid
 
1000
Diesel
 
397
E85 Flex Fuel
 
320
Other values (8)
 
35

Length

Max length16
Median length8
Mean length7.9471303
Min length6

Characters and Unicode

Total characters151067
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowElectric
2nd rowGasoline
3rd rowDiesel
4th rowGasoline
5th rowGasoline

Common Values

ValueCountFrequency (%)
Gasoline 16058
83.9%
Electric 1199
 
6.3%
Hybrid 1000
 
5.2%
Diesel 397
 
2.1%
E85 Flex Fuel 320
 
1.7%
Flexible Fuel 11
 
0.1%
Plug-In Hybrid 6
 
< 0.1%
Bio Diesel 4
 
< 0.1%
Plug-in Gas/Elec 4
 
< 0.1%
Gasoline Fuel 4
 
< 0.1%
Other values (3) 6
 
< 0.1%
(Missing) 129
 
0.7%

Length

2024-04-24T19:21:06.394124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gasoline 16062
81.6%
electric 1201
 
6.1%
hybrid 1008
 
5.1%
diesel 401
 
2.0%
fuel 337
 
1.7%
e85 320
 
1.6%
flex 320
 
1.6%
flexible 11
 
0.1%
plug-in 10
 
0.1%
bio 4
 
< 0.1%
Other values (5) 12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 18750
12.4%
i 18695
12.4%
l 18359
12.2%
s 16469
10.9%
n 16072
10.6%
G 16070
10.6%
a 16070
10.6%
o 16066
10.6%
c 2410
 
1.6%
r 2211
 
1.5%
Other values (21) 9895
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 151067
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 18750
12.4%
i 18695
12.4%
l 18359
12.2%
s 16469
10.9%
n 16072
10.6%
G 16070
10.6%
a 16070
10.6%
o 16066
10.6%
c 2410
 
1.6%
r 2211
 
1.5%
Other values (21) 9895
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 151067
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 18750
12.4%
i 18695
12.4%
l 18359
12.2%
s 16469
10.9%
n 16072
10.6%
G 16070
10.6%
a 16070
10.6%
o 16066
10.6%
c 2410
 
1.6%
r 2211
 
1.5%
Other values (21) 9895
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 151067
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 18750
12.4%
i 18695
12.4%
l 18359
12.2%
s 16469
10.9%
n 16072
10.6%
G 16070
10.6%
a 16070
10.6%
o 16066
10.6%
c 2410
 
1.6%
r 2211
 
1.5%
Other values (21) 9895
6.6%

stock_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size149.6 KiB
New
10584 
Used
8554 

Length

Max length4
Median length3
Mean length3.4469642
Min length3

Characters and Unicode

Total characters65968
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew
2nd rowNew
3rd rowNew
4th rowNew
5th rowNew

Common Values

ValueCountFrequency (%)
New 10584
55.3%
Used 8554
44.7%

Length

2024-04-24T19:21:06.569807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-24T19:21:06.710726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
new 10584
55.3%
used 8554
44.7%

Most occurring characters

ValueCountFrequency (%)
e 19138
29.0%
N 10584
16.0%
w 10584
16.0%
U 8554
13.0%
s 8554
13.0%
d 8554
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65968
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 19138
29.0%
N 10584
16.0%
w 10584
16.0%
U 8554
13.0%
s 8554
13.0%
d 8554
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65968
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 19138
29.0%
N 10584
16.0%
w 10584
16.0%
U 8554
13.0%
s 8554
13.0%
d 8554
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65968
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 19138
29.0%
N 10584
16.0%
w 10584
16.0%
U 8554
13.0%
s 8554
13.0%
d 8554
13.0%
Distinct1321
Distinct (%)7.0%
Missing170
Missing (%)0.9%
Memory size149.6 KiB
2024-04-24T19:21:06.943543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length48
Median length41
Mean length15.19127
Min length3

Characters and Unicode

Total characters288148
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)0.4%

Sample

1st rowsterling_gray_metallic
2nd rowbright_white_clearcoat
3rd rowblue_grey
4th rowradiant_red_metallic
5th rowsummit_white
ValueCountFrequency (%)
black 1118
 
5.9%
summit_white 722
 
3.8%
white 589
 
3.1%
gray 541
 
2.9%
oxford_white 370
 
2.0%
bright_white_clearcoat 352
 
1.9%
blue 321
 
1.7%
silver 303
 
1.6%
platinum_gray_metallic 272
 
1.4%
carbonized_gray_metallic 256
 
1.3%
Other values (1310) 14124
74.5%
2024-04-24T19:21:07.355890image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 29573
10.3%
e 29207
 
10.1%
a 28108
 
9.8%
_ 25657
 
8.9%
i 23803
 
8.3%
t 22639
 
7.9%
c 18645
 
6.5%
r 17494
 
6.1%
m 11499
 
4.0%
b 8074
 
2.8%
Other values (32) 73449
25.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 288148
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 29573
10.3%
e 29207
 
10.1%
a 28108
 
9.8%
_ 25657
 
8.9%
i 23803
 
8.3%
t 22639
 
7.9%
c 18645
 
6.5%
r 17494
 
6.1%
m 11499
 
4.0%
b 8074
 
2.8%
Other values (32) 73449
25.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 288148
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 29573
10.3%
e 29207
 
10.1%
a 28108
 
9.8%
_ 25657
 
8.9%
i 23803
 
8.3%
t 22639
 
7.9%
c 18645
 
6.5%
r 17494
 
6.1%
m 11499
 
4.0%
b 8074
 
2.8%
Other values (32) 73449
25.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 288148
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 29573
10.3%
e 29207
 
10.1%
a 28108
 
9.8%
_ 25657
 
8.9%
i 23803
 
8.3%
t 22639
 
7.9%
c 18645
 
6.5%
r 17494
 
6.1%
m 11499
 
4.0%
b 8074
 
2.8%
Other values (32) 73449
25.5%

page_channel
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size149.6 KiB
shopping
19138 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters153104
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowshopping
2nd rowshopping
3rd rowshopping
4th rowshopping
5th rowshopping

Common Values

ValueCountFrequency (%)
shopping 19138
100.0%

Length

2024-04-24T19:21:07.527060image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-24T19:21:07.656173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
shopping 19138
100.0%

Most occurring characters

ValueCountFrequency (%)
p 38276
25.0%
s 19138
12.5%
h 19138
12.5%
o 19138
12.5%
i 19138
12.5%
n 19138
12.5%
g 19138
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 153104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 38276
25.0%
s 19138
12.5%
h 19138
12.5%
o 19138
12.5%
i 19138
12.5%
n 19138
12.5%
g 19138
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 153104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 38276
25.0%
s 19138
12.5%
h 19138
12.5%
o 19138
12.5%
i 19138
12.5%
n 19138
12.5%
g 19138
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 153104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 38276
25.0%
s 19138
12.5%
h 19138
12.5%
o 19138
12.5%
i 19138
12.5%
n 19138
12.5%
g 19138
12.5%

Interactions

2024-04-24T19:20:53.247231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:49.249330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:50.077543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:50.895411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:51.727965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:52.570144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:53.379561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:49.391126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:50.207212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:51.056014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:51.869253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:52.696959image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:53.492144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:49.533470image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:50.366910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:51.186954image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:51.983861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:52.806856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:53.644327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:49.668721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:50.497406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:51.320735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:52.191607image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:52.912101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:53.801806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:49.824897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:50.627802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:51.474796image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:52.305300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:53.020053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:53.937031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:49.959960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:50.754224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:51.592435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:52.444544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T19:20:53.139050image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-04-24T19:20:54.154853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-24T19:20:54.605067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0msrpyearcanonical_mmtymodellocal_zoneinterior_coloraff_codepriceprice_badgetrimdrivetraindealer_namedealer_zipmileagemakebodystylecatvincanonical_mmtfuel_typestock_typeexterior_colorpage_channel
0057215.02024Chevrolet:Blazer EV:RS:2024Blazer EVNaNBlacknational54595.0NaNRSAll-wheel DriveCastle Rock Chevrolet GMC80104.00.0ChevroletSUVev_crossover_midsize3GNKDCRJ6RS227894Chevrolet:Blazer EV:RSElectricNewsterling_gray_metallicshopping
1158845.02024RAM:ProMaster 2500:High Roof:2024ProMaster 2500NaNBlacknational52446.0NaNHigh RoofFront-wheel DriveNew Smyrna Chrysler Jeep Dodge RAM32168.00.0RAMCargo Vanvan_fullsize3C6LRVDG0RE118763RAM:ProMaster 2500:High RoofGasolineNewbright_white_clearcoatshopping
2258795.02024Mercedes-Benz:Sprinter 2500:High Roof:2024Sprinter 2500NaNNaNnational54295.0NaNHigh RoofRear-wheel DriveMercedes-Benz of Farmington84025.08.0Mercedes-BenzCargo Vanvan_fullsizeW1Y4KCHY8RT178723Mercedes-Benz:Sprinter 2500:High RoofDieselNewblue_greyshopping
3333815.02024Honda:CR-V:EX:2024CR-VNaNGraynationalNaNNaNEXFront-wheel DriveKingman Honda86409.07.0HondaSUVcrossover_compact5J6RS3H44RL004214Honda:CR-V:EXGasolineNewradiant_red_metallicshopping
4427995.02024Chevrolet:Equinox:LS:2024EquinoxNaNMedium Ash Graynational24803.0NaNLSFront-wheel DriveMcSweeney Chevrolet GMC Clanton35045.00.0ChevroletSUVcrossover_midsize3GNAXHEG1RL299011Chevrolet:Equinox:LSGasolineNewsummit_whiteshopping
5583630.02024Audi:Q8 e-tron:Premium:2024Q8 e-tronNaNPearl Beigenational83630.0NaNPremiumAll-wheel DriveAudi Stuart34997.020.0AudiSUVev_crossover_midsizeWA15AAGE4RB021424Audi:Q8 e-tron:PremiumElectricNewglacier_white_metallicshopping
6633610.02024Mitsubishi:Eclipse Cross:SEL:2024Eclipse CrossNaNGraynational33610.0NaNSELFour-wheel DriveMcClinton Auto Group26101.05.0MitsubishiSUVcrossover_compactJA4ATWAA2RZ046423Mitsubishi:Eclipse Cross:SELGasolineNewmercury_gray_metallicshopping
7750185.02024Dodge:Hornet:R/T Plus:2024HornetNaNBlacknational40185.0NaNR/T PlusAll-wheel DriveDon Jackson CDJR North30028.016.0DodgeSUVhybrid_suvZACPDFDW9R3A24025Dodge:Hornet:R/T PlusHybridNewblue_steelshopping
8827825.02024Nissan:Kicks:SR:2024KicksNaNCharcoalnational27825.0NaNSRFront-wheel DriveHalladay Nissan82001.06.0NissanSUVcrossover_compact3N1CP5DV6RL526633Nissan:Kicks:SRGasolineNewscarlet_ember_tintcoatshopping
9953727.02024Volkswagen:Atlas Cross Sport:2.0T SEL Premium R-Line:2024Atlas Cross SportNaNBlack w/ Blue Crustnational50727.0NaN2.0T SEL Premium R-LineAll-wheel DriveAutoNation Volkswagen Las Vegas89146.010.0VolkswagenSUVsuv_midsize1V2FE2CA0RC238064Volkswagen:Atlas Cross Sport:2.0T SEL Premium R-LineGasolineNewplatinum_gray_metallicshopping
Unnamed: 0msrpyearcanonical_mmtymodellocal_zoneinterior_coloraff_codepriceprice_badgetrimdrivetraindealer_namedealer_zipmileagemakebodystylecatvincanonical_mmtfuel_typestock_typeexterior_colorpage_channel
191281912851410.02024Ford:Explorer:XLT:2024ExplorerNaNLight Slatenational48350.0NaNXLTFour-wheel DriveWebb Ford46322.010.0FordSUVsuv_midsize1FMSK8DH5RGA80767Ford:Explorer:XLTGasolineNewrapid_red_metallic_tinted_clearcoatshopping
1912919129220250.02024Mercedes-Benz:Maybach GLS 600:4MATIC:2024Maybach GLS 600NaNCrystal Whitenational220250.0NaN4MATICAll-wheel DriveMercedes-Benz of Westmont60559.013.0Mercedes-BenzSUVluxurysuv_suv4JGFF8HB8RB190818Mercedes-Benz:Maybach GLS 600:4MATICGasolineNewcirrus_silver_metallicshopping
191301913061395.02024Lincoln:Nautilus:Reserve:2024NautilusNaNBlack Onyxnational61395.0NaNReserveAll-wheel DriveFox Lincoln60647.02.0LincolnSUVluxurysuv_crossover5LMPJ8K46RJ801932Lincoln:Nautilus:ReserveGasolineNewinfinite_black_metallicshopping
191311913121480.02024Nissan:Versa:1.6 SV:2024VersaNaNGraphitenational21480.0NaN1.6 SVFront-wheel DriveAl Piemonte Nissan60160.010.0NissanSedansedan_compact3N1CN8EV0RL880511Nissan:Versa:1.6 SVGasolineNewgun_metallicshopping
1913219132146195.02024BMW:i7:xDrive60:2024i7NaNSmokenational146195.0NaNxDrive60All-wheel DriveBMW of Orland Park60467.08.0BMWSedanev_luxurypassenger_standardWBY53EJ04RCS04853BMW:i7:xDrive60ElectricNewmineral_white_metallicshopping
19133191330.02018BMW:X4:M40i:2018X4NaNBlack w/Gray Contrast Stitchingnational29995.0NaNM40iAll-wheel DriveChi Town Motors60445.048486.0BMWSUVluxurysuv_crossover5UXXW7C57J0Z44858BMW:X4:M40iGasolineUsedalpine_whiteshopping
19134191340.02021Mercedes-Benz:GLA 250:Base 4MATIC:2021GLA 250NaNBlacknational34750.0NaNBase 4MATICAll-wheel DriveLoeber Motors Mercedes-Benz60712.016402.0Mercedes-BenzSUVluxurysuv_crossoverW1N4N4HB3MJ172323Mercedes-Benz:GLA 250:Base 4MATICGasolineUsednight_blackshopping
191351913521480.02024Nissan:Versa:1.6 SV:2024VersaNaNGraphitenational20943.0NaN1.6 SVFront-wheel Drive94 Nissan Of South Holland60473.07.0NissanSedansedan_compact3N1CN8EV3RL885198Nissan:Versa:1.6 SVGasolineNewblackshopping
191361913653730.02024Nissan:Pathfinder:Platinum:2024PathfinderNaNNaNnational53730.0NaNPlatinumFour-wheel DriveNapleton Nissan46375.01.0NissanSUVsuv_midsize5N1DR3DJ2RC265764Nissan:Pathfinder:PlatinumGasolineNewblackshopping
1913719137NaN2021Ford:Bronco Sport:Outer Banks:2021Bronco SportNaNEbonynational25399.0NaNOuter BanksFour-wheel DriveHawk Ford of Carol Stream60188.046144.0FordSUVcrossover_compact3FMCR9C68MRA85839Ford:Bronco Sport:Outer BanksGasolineUsedblueshopping